JMIR Medical Education

Recent studies, including those by the National Board of Medical Examiners (NBME), have highlighted the remarkable capabilities of recent large language models (LLMs) such as ChatGPT in passing the United States Medical Licensing Examination (USMLE). However, there is a gap in detailed analysis of LLM performance in specific medical content areas, thus limiting an assessment of their potential utility in medical education.

Competence-based medical education requires robust data to link competence with clinical experiences. The SARS-CoV-2 pandemic abruptly altered the standard trajectory of clinical exposure in medical training programs. Residency program directors were tasked with identifying and addressing the resultant gaps in each trainee’s experiences using existing tools.

Abstract: Uncertainty is an inherent feature in the practice of medicine. Whether it is in understanding the patient’s problem, performing the physical examination, interpreting diagnostic tests or proposing a management plan, physicians are asked to make decisions on a daily basis without complete certainty. The sources of this uncertainty are widespread, and range from lack of knowledge about the patient, personal physician limitations, and limited predictive power of objective diagnostic tools. This uncertainty poses significant problems in providing competent patient care. Research efforts and teaching are attempts to reduce uncertainty that have now become inherent to medicine. Despite this, uncertainty is rampant. Artificial intelligence tools, which are being rapidly developed and integrated into practice, may change the way we navigate uncertainty. In their strongest forms, artificial intelligence tools may have the ability to improve data collection on diseases, patient beliefs, values and preferences, and allow more time for physician-patient communication. These tools hold potential to improve reducible forms of uncertainty in medicine, such as those due to lack of clinical information and provider skill and bias, using methods not previously considered. Despite this, there has been considerable resistance to the implementation of AI tools in medical practice. In this viewpoint article, we discuss the impact of artificial intelligence on medical uncertainty and discuss practical approaches to teaching the use of artificial intelligence tools in medical schools and residency training programs, including AI ethics, practical skills and technological aptitude.

Undergraduate medical students often lack hands-on research experience and fundamental scientific research skills, limiting their exposure to the practical aspects of scientific investigation. The Cerrahpasa Neuroscience Society introduced a program to address this deficiency and facilitate student-led research.

Digital transformation has disrupted many industries but is yet to revolutionise healthcare. Educational programs must be aligned to the reality that beyond developing individuals in their own professions, professionals wishing to make an impact in digital health will need a multidisciplinary understanding of how business models, organisational processes, stakeholder relationships, and workforce dynamics across the healthcare ecosystem may be disrupted by digital health technology.

Social media is a powerful platform for disseminating health information, yet it is often riddled with misinformation. Further, few guidelines exist for producing reliable, peer-reviewed content. This study describes a framework for creating and disseminating evidence-based videos on polycystic ovary syndrome (PCOS) and thyroid conditions to improve health literacy and tackle misinformation.

Critical evaluation of naloxone co-prescription academic detailing programs have been positive, but little research has focused on how participant thinking changes during academic detailing.

There is a shortage of competent health professionals in managing malnutrition. Online education may be a practical and flexible approach to address this gap.

AI Chatbots are poised to have a profound impact on medical education. Medical students, as early adopters of technology and future healthcare providers, play a crucial role in shaping the future of healthcare. However, little is known about the utilization, perception, and intention to use AI chatbots among medical students in China.

Social media (SoMe) have taken a major place in the medical field and younger generations are increasingly using them as their primary source to find information.

As part of the residency application process in the United States, many medical specialties now offer applicants the opportunity to send program signals that indicate high interest to a limited number of residency programs. To determine which residency programs to apply to, and which programs to send signals to, applicants need accurate information to determine which programs align with their future training goals. Most applicants use a program’s website to review program characteristics and criteria, so describing the current state of residency program websites can inform programs of best practices.

Although digital health is essential for improving health care, its adoption remains slow due to the lack of literacy in this area. Therefore, it is crucial for health professionals to acquire digital skills and for a digital competence assessment and accreditation model to be implemented to make advances in this field.